Expected k-coverage in wireless sensor networks

Yen, Li-Hsing; Yu, Chang Wu; Cheng, Yang

doi:10.1016/j.adhoc.2005.07.001

Cited by 88 publications

(47 citation statements)

References 17 publications

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“…An equation to calculate the coverage area given the lengths of the area, the number of nodes and the sensing radius of a node is proposed in [13]. Given that r is the sensing radius, l and m the respective lengths of the area and n the number of nodes then the expected coverage is:…”

Section: Resultsmentioning

confidence: 99%

Trading sensing coverage for an extended network lifetime

Lim

Bleakley

2010

Wmnc2010

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Section: Resultsmentioning

confidence: 99%

Trading sensing coverage for an extended network lifetime

Lim

Bleakley

2010

Wmnc2010

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“…A number of theoretical studies are also given in the literature of the k-coverage problem [32,47,[68][69][70][71][72][73][74].…”

Section: K-coverage Providing Algorithmsmentioning

confidence: 99%

“…Using the theorems and results given in [70] and by ignoring the border effect [93], N Min i ðnÞ can be computed using Eq. (14) given below.…”

Section: Applying Local Rulementioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

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“…Consequently, range-free algorithm is more suitable for sensor networks. Some range-free algorithms are presented in [8,9,10]. PEAS in [8] uses a probing mechanism for a sleeping sensor to periodically wake up to broadcast a probe message to decide whether to change the state.…”

Section: Related Workmentioning

confidence: 99%

“…If there is a reply from a working sensor, the probing sensor goes back to sleep, otherwise, it becomes a working sensor. Two probabilistic algorithms in [9,10] relies on stochastic process for density control. Although location-free density control algorithms may not achieve complete coverage, they are very useful for sensor networks where sensors have no location information.…”

Section: Related Workmentioning

confidence: 99%

A Density Control Algorithm For Wireless Sensor Network

Luo

Zhou²,

Zhang³

et al. 2011

IJWMT

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If all nodes of a sensor network worked simultaneously, not only there would be a lot of redundant information, but also they would have great adverse impact on network throughput, bandwidth, latency, energy and network lifetime. Consequently, density control technology is necessary for sensor networks, because it can reduce the number of active sensor nodes under the precondition of ensuring network coverage and network connectivity. This paper proposes SNDC (Sensor Network Density Control), a location-free and range-free density control algorithm for wireless sensor network to keep as few as possible sensors in active state to achieve an optimal complete connected coverage of a specific monitored area by periodically sending three beacons of different transmission ranges. Inactive sensors can turn off sensing modules to save energy and sleep. Simulation results show that this algorithm can prolong the network lifetime and guarantee the small number of active nodes and complete network coverage.

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Expected k-coverage in wireless sensor networks

Cited by 88 publications

References 17 publications

Trading sensing coverage for an extended network lifetime

Trading sensing coverage for an extended network lifetime

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

A Density Control Algorithm For Wireless Sensor Network

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